Systems and methods for retrospective home value scoring

ABSTRACT

Systems and methods are provided for providing, based on a model, an indication that an appraisal value for a property is likely to be faulty. In one embodiment, a method includes receiving information representative of at least one of a borrower, a property, or one or more demographics, such that the received information corresponds to a date. The method determines a score based on the received information and the model, such that the score provides the indication of the likelihood that the appraisal value was faulty on the date.

CROSS REFERENCE TO RELATED APPLICATION

This is a division of application Ser. No. 10/679,516, filed Oct. 7,2003 now U.S. Pat. No. 7,711,574, which is a continuation-in-part ofU.S. application Ser. No. 10/094,806, entitled “SYSTEMS AND METHODS FORHOME VALUE SCORING,” filed Mar. 12, 2002, which claims the benefit ofU.S. Provisional Patent Application No. 60/311,125, entitled “SYSTEMSAND METHODS FOR HOME VALUE SCORING,” filed on Aug. 10, 2001. Thisapplication is related to U.S. patent application Ser. No. 09/134,161,entitled “SYSTEM AND METHOD FOR PROVIDING PROPERTY VALUE ESTIMATES,”filed on Aug. 14, 1998, which is based on U.S. Provisional ApplicationSer. No. 60/056,196, filed on Aug. 21, 1997. U.S. patent applicationSer. No. 09/134,161 is itself a continuation-in-part of U.S. patentapplication Ser. No. 08/730,289, entitled “METHOD FOR COMBINING HOUSEPRICE FORECASTS,” filed on Oct. 11, 1996. The present invention alsorelates to U.S. patent application Ser. No. 10/095,006, entitled “SYSTEMAND METHODS FOR GENERATING A MODEL FOR HOME VALUE SCORING,” filed onMar. 12, 2002. All of the above-identified applications are expresslyincorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION

I. Field of the Invention

The present invention generally relates to financial systems and tosystems and methods for processing financial information. Moreparticularly, the invention relates to systems and methods forevaluating the likelihood that an appraisal of property is faulty.

II. Background and Material Information

When an application for a mortgage loan is processed by a financialentity such as a lender, bank, mortgage bank, mortgage broker, ormortgage originator, the property securing the mortgage is usuallyappraised. Since various financial entities have an interest in knowingthe true (or fair) market value of the property securing the mortgage,the appraisal of a property is an important part of the mortgage loanapplication process.

An appraisal provides a property value estimate indicating a marketvalue for a property. The appraisal may be performed in various waysincluding, for example, an in-person property appraisal performed by anappraiser. During the in-person appraisal, the appraiser physicallyinspects the property. With or without a physical inspection of theproperty, recent sales information for comparable properties may be usedto generate an appraisal.

Alternatively, an automated valuation model serves as a tool thatutilizes various factors (e.g., ZIP code, lot size, number of bedrooms,etc.) to appraise a property. Examples of automated valuation models maybe found in one or more of the following applications: U.S. patentapplication Ser. No. 08/730,289, filed on Oct. 11, 1996, entitled“METHOD FOR COMBINING HOUSE PRICE FORECASTS”; U.S. patent applicationSer. No. 09/115,831, filed on Jul. 15, 1998, entitled “SYSTEM AND METHODFOR PROVIDING HOUSE PRICE FORECASTS BASED ON REPEAT SALES MODEL” (nowU.S. Pat. No. 6,401,070); U.S. patent application Ser. No. 09/134,161,filed on Aug. 14, 1998, entitled “SYSTEM AND METHOD FOR PROVIDINGPROPERTY VALUE ESTIMATES”; U.S. patent application Ser. No. 09/728,061,filed on Dec. 4, 2000, entitled “METHOD FOR FORECASTING HOUSE PRICESUSING A DYNAMIC ERROR CORRECTION MODEL”; all of which are herebyincorporated by reference in their entirety. Other types of appraisalsthat provide an informed estimate of property value may also be used toappraise a property. Because of the various forms that an appraisal maytake, an appraisal may be burdensome for a financial entity to processand/or interpret. For example, a financial entity may find it difficultto readily assess the reliability of an appraisal and, as a result,order an unnecessary reappraisal of the property.

A financial entity may use an appraisal as part of its mortgage loanapproval process. For example, when a borrower applies for a mortgageloan, the appraisal may be used by a bank to verify the value of theunderlying property. The bank uses the property value as a factor inapproving or rejecting the mortgage loan application. For example, whenan appraisal indicates that a property is worth less than the mortgageamount, a bank may not be willing to accept the financial risk and willtherefore reject the mortgage loan application. On the other hand, thebank may simply tell the borrower that the maximum amount borrowedcannot exceed the appraised property value, or a percentage thereof. Forthese and other reasons, the appraisal is usually considered animportant part of the mortgage loan application process.

SUMMARY OF THE INVENTION

Accordingly, the present invention is directed to systems and methodsfor processing financial information and, more particularly, systems andmethods for evaluating the likelihood that an appraisal of property isfaulty.

A financial system consistent with the systems and methods of thepresent invention may provide, based on a model, an indication that anappraisal value for a property is likely to be faulty including, forexample, receiving information representative of at least one of aborrower, a property, or one or more demographics; receiving theappraisal value of the property; and determining a score based on thereceived information, received appraisal, and the model, such that thescore provides the indication of the likelihood that the appraisal valuefor the property is faulty.

Additional features and advantages of the invention will be set forth inpart in the following description and in part will be obvious from thedescription, or may be learned by practice of the invention. Theobjectives and advantages of the invention may be realized and attainedby the system and method particularly described in the writtendescription and claims hereof as well as the appended drawings.

To achieve these and other advantages and in accordance with the purposeof the invention, as embodied and broadly described herein, there isalso provided a method for providing an indication, based on a model,that an appraisal value for a property, which is secured by a mortgageloan, was likely to be faulty. The method includes, for example,receiving a date; receiving information representative of at least oneof a borrower, a property, or one or more demographics, such that thereceived information corresponds to the date; receiving the appraisalvalue based on the date; and determining a score based on the receivedinformation, received appraisal, and the model, such that the scoreprovides the indication of the likelihood that the appraisal value wasfaulty on the date.

It is to be understood that both the foregoing general description andthe following detailed description are exemplary and explanatory onlyand are not restrictive of the invention, as described. Further featuresand/or variations may be provided in addition to those set forth herein.For example, the present invention may be directed to variouscombinations and subcombinations of the disclosed features and/orcombinations and subcombinations of several further features disclosedbelow in the detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate various embodiments and aspectsof the present invention and, together with the description, explain theprinciples of the invention. In the drawings:

FIG. 1 illustrates an exemplary system environment consistent with thesystems and methods of the present invention;

FIG. 2 is an exemplary block diagram for providing an indication that anappraisal value for a property is likely to be faulty using a modelconsistent with the systems and methods of the present invention;

FIG. 3A is an exemplary flowchart for providing a home value score basedon a model consistent with the systems and methods of the presentinvention;

FIG. 3B is an exemplary flowchart for generating a model consistent withthe systems and methods of the present invention;

FIG. 4 illustrates another exemplary system environment consistent withthe systems and methods of the present invention;

FIG. 5 is another exemplary flowchart for providing a home value scoreconsistent with the systems and methods of the present invention;

FIG. 6 illustrates exemplary information received from a lenderconsistent with the systems and methods of the present invention;

FIG. 7 illustrates additional information that may be receivedconsistent with the systems and methods of the present invention;

FIG. 8 depicts an exemplary web page interface for providing informationconsistent with the systems and methods of the present invention;

FIG. 9 shows an exemplary model for determining an indication that anappraisal value for a property is likely to be faulty consistent withthe systems and methods of the present invention;

FIG. 10 depicts an exemplary web page interface for receiving anindication that an appraisal value for a property is likely to be faultyconsistent with the systems and methods of the present invention;

FIG. 11 is another exemplary flowchart for generating a model consistentwith the systems and methods of the present invention;

FIG. 12 is an exemplary flowchart for determining the one or moreparameters (or coefficients) of the model consistent with the systemsand methods of the present invention;

FIG. 13 shows an exemplary table of information for determining the oneor more parameters of the model consistent with the systems and methodsof the present invention;

FIG. 14 shows another exemplary system environment consistent with thesystems and methods of the present invention;

FIG. 15 is an exemplary flowchart illustrating a process for determininga property value estimate as of a user-specified date consistent withthe systems and methods of the present invention; and

FIG. 16 is an exemplary flowchart illustrating the use of a propertyvalue estimate as of a date to classify a loan's original property valueestimate consistent with the systems and methods of the presentinvention.

DETAILED DESCRIPTION

Reference will now be made in detail to the invention, examples of whichare illustrated in the accompanying drawings. Wherever possible, thesame reference numbers will be used throughout the drawings to refer tothe same or like parts.

Systems and methods consistent with the present invention permit afinancial entity, using a computing platform (or computer), to determinean indication of whether a property appraisal is likely to be faulty.Moreover, the financial entity may determine such indication in the formof a score, which is referred to herein as a Home Value (HV) Score. Inone aspect of the invention, the computing platform determines the HVScore based on a model and scales the score into a range (e.g., 300 to900) with a low score indicating a higher likelihood that the appraisalis faulty (i.e., the appraisal has a higher probability of being bad)and a high score indicating a low likelihood that an appraisal is faulty(i.e., the appraisal is probably “good”).

By way of example only, a financial entity, such as a lender, bank,mortgage bank, mortgage broker, or mortgage originator, may process aborrower's mortgage loan application. That application may require anappraisal that is used to approve the mortgage loan application. Forexample, a mortgage originator may use the appraisal to determine aratio of the loan amount to the property value (referred to as aloan-to-value ratio or LTV) with the appraisal serving as the propertyvalue. Based on the loan-to-value ratio, the mortgage originator mayapprove the mortgage loan. However, if the appraisal is faulty, themortgage originator may then incorrectly approve the mortgage loanapplication. The mortgage originator thus has an interest in ensuringthat if the appraisal is faulty, the appraisal is discounted and a “new”appraisal (or other method of valuation) is performed. Accordingly, byusing the HV Score, a financial entity may readily assess thereliability of a property appraisal reducing the burden associated withprocessing and/or interpreting the appraisal. Moreover, because the HVScore makes it easier to interpret the reliability of the appraisal, thefinancial entity will be less likely to misinterpret an appraisal and/orrequest an unnecessary reappraisal.

In addition to evaluating an appraisal associated with an individualmortgage loan secured by real property, the HV Score may be used toevaluate each appraisal in a pool of mortgage loans. By way of exampleonly, quality control (QC) of the mortgage pool may be more accuratelytargeted to those mortgage loans secured by properties whose appraisalshave lower HV Scores. Rather than randomly selecting mortgage loans inthe pool for QC verification of an acceptable loan-to-value ratio, afinancial entity may use the HV Score to identify only those loans inthe pool most likely to have an unacceptable ratio. Accordingly, thefinancial entity may initiate further investigation (e.g., order a “new”appraisal) only of the mortgage loans identified based on thecorresponding HV Scores, reducing the financial and administrativeburden on the financial entity.

Furthermore, the HV Score may be used retrospectively to evaluate one ormore mortgage loans as of a date. For example, a financial entity, suchas a lender, may use the HV Score to retrospectively assess thereliability of a property appraisal as of a date, such as when themortgage loan closed. In this example, the HV Score retrospectivelyprovides an indication of whether the property appraisal used to closethe mortgage loan was faulty.

In situations when a mortgage loan defaults (or is delinquent with latepayments), the lender may perform a quality control function thatassesses whether the defaulting mortgage loan was also the subject of afaulty appraisal. In those situations, the lender would receive an HVScore as of the date the mortgage closed, such that the lender may beable to identify a possible factor that contributed to default (ordelinquency).

FIG. 1 shows an exemplary system 1000 for providing an indication ofwhether an appraisal is likely to be faulty, such that the indicationprovides an HV Score, enabling a financial entity to readily determinethe likelihood that the appraisal is likely to be faulty.

Referring to FIG. 1, the system includes a communication channel 1400,one or more lenders 1500,1510, one or more borrowers 1610, 1620, abroker 1700, one or more information sources 1800, and a processor 1350.The one or more lenders may include a financial entity, such as a bank,mortgage bank, mortgage broker, mortgage originator, and/or any otherentity (or individual) seeking an indication of whether an appraisalvalue of a property is likely to be faulty. The one or more borrowers1610, 1620 may include an entity, such as a consumer, seeking a mortgageloan. The broker 1700 may include an entity that acts as an agent, suchas a mortgage broker. The information source 1800 may include internal,external, proprietary, and/or public databases, such as financialdatabases and demographic databases. For example, sources of informationmay include DataQuick Information Systems, International Data ManagementInc., First American Corporation, county property and/or tax records,TransUnion LLC, Equifax Inc., Experian, Department of Commerce, andBureau of Labor and Statistics. The processor 1350 may include an entitycapable of processing information such that an HV Score is provided to,for example, a lender 1500, borrower 1610, broker 1700 and/or any otherentity requesting an HV Score.

Although the communication channel 1400 is depicted in FIG. 1 asbi-directional, a skilled artisan would recognize that unidirectionalcommunication links may be used instead.

FIG. 2 depicts a functional block diagram associated with providing anHV Score consistent with the systems and methods of the presentinvention. Referring to FIGS. 1 and 2, an entity, such as a lender 1500providing a loan secured by a property, may provide the processor 1350with information, such as information describing the borrower 205,information describing the property 210, and/or information describingthe value of the property (e.g., an appraisal) 215. The processor 1350may then use a model 220 to determine an HV Score. The processor 1350may also provide the lender 1500 with the HV Score via a communicationchannel 1400.

In one aspect of the invention, the HV Score is scaled such that a lowHV Score indicates that an appraisal, provided by the lender, is morelikely to be faulty (i.e., the property appraisal does not reflect thefair market value). On the other hand, a high HV Score indicates that anappraisal is unlikely to be faulty (i.e., the property appraisalaccurately reflects the fair market value). Accordingly, the HV Scorefacilitates determining whether an appraisal for a property is likely tobe faulty. Thus, the lender may more accurately evaluate the borrower'smortgage loan application.

FIG. 3A is an exemplary flowchart depicting steps for providing an HVScore. Referring to FIGS. 1 and 3A, in one embodiment, the processor1350 may receive information (step 3100) through a communication channel1400 from a mortgage originator, such as a lender. The receivedinformation may include, for example, information describing a borrower,a property, and an appraisal. In one aspect of the invention, theinformation describing the borrower may include the borrower's name,address, and credit history. Moreover, the property informationdescribes the property and may include, for example, the address of theproperty securing the mortgage. Furthermore, the appraisal informationprovides an estimate of the property value and may include, for example,any indication of property value, such as a borrower's estimate of theproperty value, a lender's estimate of the property value, a purchaseprice, an in-person appraisal by an appraiser, and/or an automatedvaluation model appraisal (or estimate) of the property's value.

The processor 1350 may then determine an HV Score, using the informationreceived from the mortgage originator, based on a model (step 3200). Theprocessor 1350 may also provide the HV Score (step 3300) to the mortgageoriginator, such as lender 1500, through the communication channel 1400.The lender 1500 may then use the HV Score to determine whether theappraisal information for a property is more likely to be faulty. If theappraisal is likely to be faulty, the lender may then decide to orderanother appraisal.

In one embodiment, the processor 1350 may scale an HV Score such thatthe score falls within a range, such as 300-900. Table 1 shows threeexemplary HV Scores with a likelihood that an appraisal is faulty and aproposed action for the lender 1500. For example, when the processor1350 provides an HV Score of 500 to the lender 1500, the HV Score mayindicate that a property appraisal is highly likely to be unreliable (orfaulty). With an HV Score of 500, the HV Score may lead a lender 1500 toconclude that a review of the appraisal is appropriate, such asrequesting another appraisal (e.g., an in-person appraisal by anotherappraiser).

When the processor 1350 provides an HV Score of 600 to the lender 1500,the HV Score of 600 may indicate that a property appraisal is somewhatless likely to be unreliable than a score of 500. In this case, thelender 1500 may conclude that another appraisal is appropriate. However,since the HV Score is on the borderline of being reliable, the lender1500 may simply request a less costly automated appraisal using anautomated valuation model (AVM), such as Home Value Explorer™ (HVE). Thelender 1500 may then review the output from the AVM (including recentcomparable sales in the neighborhood) to determine the reasonableness ofthe appraisal.

When the processor 1350 provides an HV Score of 700 to the lender 1500,the HV Score of 700 may indicate that a property appraisal is likely tobe more reliable than the score of 600. In this case, the lender 1500may be confident that the property appraisal is reliable. Accordingly,the lender 1500 may conclude that a reappraisal is unnecessary.

Although Table 1 shows three HV Scores between 500 and 700, any otherrange of HV Scores may be used instead including, for example, a rangeof HV Scores from 1 to 10 or 300 to 900. Moreover, although Table 1shows a lower likelihood of a faulty appraisal at higher HV Scores, askilled artisan would recognize that a lower likelihood of a faultyappraisal may be represented with lower HV Scores instead.

TABLE 1 Exemplary HV Scores HV SCORE FOR LIKELIHOOD OF A PROPERTY FAULTYAPPRAISAL PROPOSED ACTION 500 High Order a review of the appraisal 600Medium Order an appraisal using an automated valuation model and/orreview comparable recent sales (if any) 700 Low Do nothing

FIG. 3B is an exemplary flowchart depicting steps for generating amodel, such as an HV Score model, capable of providing an HV Score.Referring to FIGS. 1 and 3B, in one embodiment, the processor 1350 maybegin by receiving information from various sources of information(e.g., information source 1800) to enable the processor 1350 to generatethe HV Score model (step 3500). The processor 1350 may then process thereceived information (step 3600) to determine the coefficients (alsoreferred to as weights) that make up the HV Score model. The processor1350 may then provide the HV Score model to one or more entities (e.g.,lenders 1500, 1510 and/or brokers 1700) to permit those entities todetermine the HV Scores for appraisals. Referring again to FIG. 2, theHV Score model may thus be used as a model 220 to determine an HV Score230. Although an HV Score model is described herein, a skilled artisanwould recognize that any type of model that provides a score may be usedinstead.

In one aspect of the invention, the processor 1350 may periodically(e.g., yearly, monthly, etc.) update the HV Score model by providing anupdated set of HV Score model coefficients (step 3800).

FIG. 4 illustrates another exemplary system 4000 environment consistentwith the systems and methods of the present invention. As illustrated inFIG. 4, the system environment 4000 includes a processor 1350, one ormore lenders 1500, 1510, one or more borrowers 1610, 1620, one or morebrokers 1700, one or more information sources 1800, and a communicationchannel 1400. The processor 1350 may also include an input module 4100,an output module 4200, a computing platform 4300, and one or moredatabases 4600.

In one embodiment consistent with FIG. 4, the computing platform 4300may include a data processor such as a PC, UNIX server, or mainframecomputer for performing various functions and operations. Computingplatform 4300 may be implemented, for example, by a general purposecomputer or data processor selectively activated or reconfigured by astored computer program, or may be a specially constructed computingplatform for carrying-out the features and operations disclosed herein.Moreover, computing platform 4300 may be implemented or provided with awide variety of components or systems including, for example, one ormore of the following: one or more central processing units, aco-processor, memory, registers, and other data processing devices andsubsystems.

Communication channel 1400 may include, alone or in any suitablecombination a telephony-based network, a local area network (LAN), awide area network (WAN), a dedicated intranet, the Internet, or awireless network. Further, any suitable combination of wired and/orwireless components and systems may be incorporated into communicationchannel 1400. Although the computing platform 4300 may connect to thelenders 1500, 1510 through the communication channel 1400, computingplatform 4300 may connect directly to the lenders 1500, 1510.

Computing platform 4300 also communicates with input module 4100 and/oroutput module 4200 using connections or communication links, asillustrated in FIG. 4. Alternatively, communication between computingplatform 4300 and input module 4100 or output module 4200 may beachieved using a network (not shown) similar to that described above forcommunication channel 1400. A skilled artisan would recognize thatcomputing platform 4300 may be located in the same location or at ageographical separate location from input module 4100 and/or outputmodule 4200 by using dedicated communication links or a network.

Input module 4100 may be implemented with a wide variety of devices toreceive and/or provide information. Referring to FIG. 4, input module4100 may include an input device 4110, a storage device 4120, and/or anetwork interface 4130. Input device 4110 may also include a keyboard, amouse, a disk drive, telephone, or any other suitable input device forreceiving and/or providing information to computing platform 4300.Although FIG. 4 only illustrates a single input module 4100, a pluralityof input modules 4100 may also be used.

Storage device 4120 may be implemented with a wide variety of systems,subsystems and/or devices for providing memory or storage including, forexample, one or more of the following: a read-only memory (ROM) device,a random access memory (RAM) device, a tape or disk drive, an opticalstorage device, a magnetic storage device, a redundant array ofinexpensive disks (RAID), and/or any other device capable of providingstorage and/or memory.

Network interface 4130 may exchange data between the communicationchannel 1400 and computing platform 4300 and may also exchange databetween the input module 4100 and the computing platform 4300. In oneaspect of the invention, network interface 4130 may permit a connectionto at least one or more of the following networks: an Ethernet network,an Internet protocol network, a telephone network, a radio network, acellular network, or any other network capable of being connected toinput module 4100.

Output module 4200 may include a display 4210, a printer 4220, and/or anetwork interface 4230. The output module 4200 may be used to provide,inter alia, an HV Score to lenders 1500, 1510, provide an HV Score modelto a computing platform 4300, and/or provide the HV Score model to anyentity (or processor) seeking to determine an HV Score. Further, theoutput from computing platform 4300 may be displayed or viewed throughdisplay 4210 (e.g., a cathode ray tube or liquid crystal display) and/orprinter device 4220. For example, the HV Score may be viewed on display4210 and/or printed on printer device 4220. Although FIG. 4 onlyillustrates a single output module 4200, a plurality of spatiallyseparated output modules 4200 may be used.

The printer device 4220 may provide output that includes informationthat summarizes multiple HV Scores. The summary information may includeaverage HV Scores, percentage of HV Scores that fall above or below athreshold score, and/or other tabular/graphical information forsummarizing multiple HV Scores. Moreover, multiple HV Scores and thecorresponding summary information may also be categorized based onstate, lender (or lender branch), appraiser, or other user definedcategories.

Network interface 4230 exchanges data between the output module 4200 andthe computing platform 4300 and/or between the computing platform 4300and the communication channel 1400. The network interface 4230 maypermit connection to at least one or more of the following networks: anEthernet network, and Internet protocol network, a telephone network, acellular network, a radio network, or any other network capable of beingconnected to output module 4200.

The database 4600 may store information including financial information,demographic information, real estate information, credit information,and other public and/or proprietary information that is kept within anentity or organization. For example, the database 4600 may storeinformation received from the information source 1800 such asinformation from DataQuick Information Systems, International DataManagement Inc., First American Corporation, county property and/or taxrecords, TransUnion LLC, Equifax Inc., Experian, Department of Commerce,and Bureau of Labor and Statistics. Although the database 4600 is shownin FIG. 4 as being located with the computing platform 4300, a skilledartisan would recognize that the database (or databases) may be locatedanywhere (or in multiple locations) and connected to the computingplatform via direct links or networks.

FIG. 5 shows another exemplary flowchart with steps for providing the HVScore. Referring to FIGS. 4 and 5, the computing platform 4300 begins(step 5005) when it receives information (step 5100). For example, thecomputing platform 4300 may receive information provided by the lenders1500, 1510 through the communication channel 1400. The computingplatform 4300 may also receive information from other sources, such asthe information source 1800 and/or database 4600 (step 5200); initializeone or more variables for use in the HV Score model (step 5300);determine the HV Score based on the HV Score model and receivedinformation (step 5400); and end when it provides the HV Score (steps5500-5600).

To receive information provided by the lender (step 5100), the computingplatform 4300 may receive from the lender, such as lender 1500,information representative of a borrower, the property, or demographicinformation. In one aspect of the invention, the computing platform 4300may receive information from the lender 1500 through the communicationchannel 1400. This received information may include the informationdepicted in FIG. 6.

Referring to FIG. 6, the received information from the lender 1500 mayinclude one or more of the following: a loan reference number; theidentity of a lender; a street address of a property; the city, state,and ZIP code of the property; a stated value of the property (e.g., anappraisal provided by the borrower or the lender); an amountcorresponding to the total amount borrowed or secured by the property;and information indicating whether the mortgage loan is secured by acondominium, town house, single family home, 2-4 unit dwelling, ormultifamily dwelling. Moreover, the information received from the lender1500 may include information indicating the mortgage loan type, such aswhether the mortgage loan is for the purchase of a property, a mortgagerefinancing, a mortgage refinancing with cash returned to the borrower(referred to as a “cash out” refinance), a home improvement loan, a debtconsolidation loan, or any other type of mortgage loan. Furthermore, thereceived information may include an indication of the borrower's creditworthiness, such as a credit history or credit score(s); a flagindicating a source of the borrower credit information (e.g., creditinformation provided by a lender or a source of credit information);and/or any other information describing the borrower, the property, ordemographics associated with the borrower or the property.

Moreover, the stated value of the property may correspond to anappraisal value of the property. As noted above, the appraisal value ofa property may be any type of property appraisal including, for example,an in-person property appraisal, a borrower's property value estimate, alender's property value estimate, a sales price for the property, a loanamount, an automated valuation model (AVM) appraisal.

Referring again to FIG. 5, to receive information from other sources(step 5200), the computing platform 4300 may interface with, or beembedded in, one or more systems (not shown), that provide financialinformation, credit information, and/or real estate information, such assystems that are used to originate loans, provide appraisals (or valueproperty), and/or provide quality control tools for the mortgage loanprocess.

In one aspect of the invention, the computing platform 4300 may alsointerface with one or more sources of information (e.g., database 4600and/or the information source 1800). The sources of information mayprovide, inter alia, median home price information for a region,borrower credit information (e.g., credit reports or credit scores),property appraisal information for one or more properties, and/or anyother information that may be a factor in determining the accuracy orreliability of a property appraisal.

The sources of information may also provide the computing platform 4300with the information listed in FIG. 7. Referring to FIG. 7, theinformation received by the computing platform 4300 may include one ormore of the following: a borrower's credit score(s); a ZIP code with itsplus 4 extension (if available) for a property; an AVM estimate, astandard deviation for the AVM estimate; and/or a value corresponding toa median price for a property in a region (listed in FIG. 7 as a “zonepoint value”). The region may correspond to a street, a neighborhood, acity, a ZIP code, a county, a census tract, a metropolitan statisticalarea, a state, and/or a country.

In one aspect of the invention, the information depicted in FIG. 6 isprovided via a web-based input. FIG. 8 shows a web page for providinginformation to a processor 1350 (or computing platform 4300) via thecommunication channel 1400 (e.g., the Internet). A lender, a borrower, abroker, and/or any other entity seeking an HV Score may access the webpage of FIG. 8 to provide processor 1350 (or computing platform 4300)with information. The information provided via the web page of FIG. 8may then be used by the computing platform 4300 to determine the HVScore.

Referring again to FIG. 5, to initialize variables for use in the HVScore model (step 5300), the computing platform 4300 may initialize oneor more of the variables in the HV Score model based on the informationreceived in steps 5100 and 5200. In one embodiment, the computingplatform 4300 may initialize the variables, such as the variables listedin Table 2 below.

Referring to Table 2, the computing platform 4300 may initialize one ormore variables based on the received information in steps 5100-5200. Forexample, the computing platform may initialize the variable “CS” with acredit score received from the lender. The variable “CS 660” and “CS760” adjusts the sensitivity of the HV Score when the variable “CS” isabove 660 or 760, respectively. The variable “MCRED” may provide a flagindicating that the credit score is missing. The variable “LTV”(loan-to-value) may correspond to the ratio of the loan amount to thefair market value of the property multiplied by 100. For example, amortgage of $100,000 on a property valued at $200,000 would have an“LTV” of 50. The variables “LTV 71,” LTV 81,” and “LTV 91” adjust thesensitivity of the HV Score to the variable “LTV” when “LTV” exceeds 70,80, or 90, respectively.

In addition, the computing platform 4300 may initialize variables basedon the purpose (or type) of mortgage loan. For example, the variable“CONDO” may correspond to a flag that is a “1” when the property type isa condominium. The variable “PUR” may indicate that the mortgage purposecorresponds to a mortgage only for a property purchase (e.g., withoutcash out). The variable “NCO” may correspond to a “1” when the mortgagepurpose corresponds to a rate/term refinancing. The variable “CO” may beset to a value of “1” when the mortgage purpose corresponds to a cashout refinance (i.e., cash returned to the borrower). The variable “HIL”may indicate that the mortgage purpose corresponds to a home improvementloan. The variable “DC” may indicate that the mortgage purpose includesa borrower's debt consolidation mortgage loan. The variable “OTH” mayindicate that the mortgage purpose is unknown or other than the onesmentioned above.

TABLE 2 Initialized Variables CS = credit score expressed in integers,e.g. 715 CS660 = max(CS-660,0) CS760 = max(CS-760,0) MCRED = 1 if CreditScore is missing, otherwise 0; LTV = total loan-to-value ratio,expressed as an integer, e.g. 80    So (total loan amt/stated value)*100LTV71 = max(LTV-70,0) LTV81 = max(LTV-80,0) LTV91 = max(LTV-90,0) CONDO= 1 if property is Condominium, else 0 PUR = 1 if purpose is purchase,else 0 NCO = 1 if purpose is rate/term refinance, else 0 CO = 1 ifpurpose is cash out refinance, else 0 HIL = 1 if purpose is homeimprovement loan, else 0 DC = 1 if purpose is debt consolidation, else 0OTH = if purpose is other, else 0 VALSIG = (log(stated value) −log(CPV))/St dev. (Logs are natural logs) If VALSIG > 0 then VALSIGU =VALSIG. Else VALSIGU = 0 If VALSIG < 0 then VALSIGD = VALSIG. ElseVALSIGD = 0 ZONDIF = (log(stated value) − log(Zone Point Value)). IfZONDIF > 0 then ZONDIFU = ZONDIF. Else ZONDIFU = 0 If ZONDIF < 0 thenZONDIFD = ZONDIF. Else ZONDIFD = 0 If Hedonic Point Value >10,000 andRepeat Sales Point Value > 10,000 then NOTBOTH = 0;    ELSE NOTBOTH = 1;VSU2 = max(VALSIGU-2.0,0);

The variable “VALSIG” may be determined based on the following equation:VALSIG=(log(stated value)−log(CPV))/St dev  Equation 1where CPV means combined point value; where “St dev” represents thestandard deviation associated with the property valuations provided byan automated valuation model (AVM), such as HVE. The variable VALSIGrepresents the number of standard deviations (or sigmas (a)) between thestated value (or appraisal) of the property and the estimated valueprovided by the AVM. In this example, the AVM provides a propertyvaluation estimate that is just one of the numerous factors consideredwhen determining the HV Score.

The variables “VALSIGU” and “VALSIGD” may be determined based on thevalue of “VALSIG,” as shown in Table 2. The value of the variable“ZONDIF” may be determined based on the following equation:ZONDIF=(log(stated value)−log(Zone Point Value))  Equation 2

The values of variable “ZONDIFU” and “ZONDIFD” may be determined basedon the value of “ZONDIF,” as shown in Table 2. Further, the variable“NOTBOTH” may be set to a value of zero when two types of automatedvaluation models are used (e.g., using a valuation model based on repeatsales and one based on hedonics), while “NOTBOTH” is set to a value ofone when only a single type of automated valuation model is used.

The variable “VSU2” may be determined based on the value of the variable“VALSIGU,” as shown in Table 2. The variable VSU2 adjusts thesensitivity of the HV Score with respect to the VALSIG value of Equation1.

To determine the HV Score (step 5400 of FIG. 5), the computing platform4300 may use the HV Score model to compute the HV Score. Referring againto FIG. 2A, the HV Score model, may produce an HV Score 230 based on oneor more of the following: borrower information 205, property information210, and an appraisal value 215. In one aspect of the invention, thecomputing platform may determine an HV Score by multiplying theinitialized one or more variables from step 5300 by one or morecorresponding coefficients (or weights) that are part of the HV Scoremodel 220. Moreover, the computing platform 4300 may scale the HV Scoreinto a predetermined range, such as the range of 300 to 900. Thecomputing platform 4300 may then provide the scaled score as the HVScore to the lender or other entity that requested the score (step5500).

In one embodiment of the present invention, the HV Score provided abovein step 5500 is retrospective in that the lender (or user) specifies adate (also referred to herein as an “as of” date) for the HV Score. Inthat embodiment, the computing platform 4300 may receive from the lenderthe date and information corresponding to that date (e.g., informationrepresentative of a borrower, the property, or demographic information)(step 5100). For example, the date may represent when a mortgage loanclosed. In this example, the HV Score for that date indicates thefaultiness of the appraisal as of the date the mortgage loan closed.

Referring again to FIG. 6, the received information from the lender 1500may include the as of date (not shown). Moreover, the informationreceived from the lender, such as the information depicted in FIG. 6,may be information as of the date specified by the lender. For example,a lender (or user) may provide the information depicted in FIG. 6 for amortgage loan that closed in 1997. When that is the case, the lender mayselect the as of date as 1997, the Borrower's credit score as of 1997,and one or more of the information (depicted in FIG. 6) for the 1997timeframe. The received information for 1997 may be associated with themortgage loan documents from 1997.

The computing platform 4300 may also receive the information listed inFIG. 7 based on the as of date specified by the lender (step 5200). Forexample, the AVM estimate, the standard deviation for the AVM estimate,and/or the zone point value (all described above) may be based on the1997 timeframe specified by the lender. For example, if the lenderspecifies “1997” and provides a property address of “100 Home Sweet HomeStreet, AnyWhere, N.Y., 14621-9999,” the AVM uses data corresponding to1997 for that property. The AVM then provides a 1997 price estimate, astandard deviation based on that price estimate (i.e., the AVM standarddeviation), and an indication of the median price for the regionassociated with the property (also referred to herein as zone pointvalue).

To determine the HV Score as of the date specified by the lender, thecomputing platform may then initialize one or more variables for use inthe HV Score model based on the information received in steps 5100 and5200, as described above for step 5300. In one embodiment, the computingplatform 4300 may use a current (or recent) HV Score model.Alternatively, a previous (or old) HV Score model, such as an HV Scoremodel corresponding to the date specified by the lender may be usedinstead.

The computing platform 4300 then determines the HV Score and providesthe HV Score for the as of date specified by the lender (steps5400-5600). Accordingly, a lender may receive a retrospective HV Scoreon a mortgage loan that is performing poorly (e.g., in default or latepayments)—enabling the lender to assess whether the appraisal used toclose the mortgage was faulty. Although reference is made in thisembodiment to a lender, any financial entity can assess the reliabilityof an appraisal as of a date including, for example, auditors andquality control departments. Moreover, although reference is made to asingle mortgage, retrospective HV Scores can also be provided on aplurality of mortgage loans, such as a pool of mortgages.

Although the as of date may represent an actual date, such as theclosing date of a mortgage loan, the as of date may also represent anyother date including a timeframe (or time period) or an approximatedate. For example, the as of date may correspond to the year, month,and/or date (e.g., 1997, August, and/or Aug. 7, 1997). Moreover, askilled artisan would recognize that an appraisal used for a mortgageloan may have been performed before the mortgage close (and in somecases up to a year before closing). In that case, the as of dateprovided by the lender may correspond to the closing date of themortgage loan, the appraisal date, or any other date.

FIG. 9 shows an exemplary model, such as an HV Score model. Referring toFIG. 9, the computing platform 4300 may determine the product of theinitialized variable and corresponding model coefficient (lines 1-21).For example, computing platform 4300 would determine the product of thecoefficient “−11.0280” and the initialized variable “LTV” by multiplyingthese two values (line 6). As illustrated in FIG. 9, the computingplatform 4300 then sums all of the determined products to produce an HVScore.

Moreover, in one aspect of the invention, an HV Score is scaled into arange of 300 to 900 such that an HV Score of 300 suggests that anappraisal value received from a lender may be faulty. On the other hand,an HV Score of 900 would indicate that the appraisal value is likely tobe reliable. FIG. 9 at lines 23-24 shows that an HV Score that is lessthan 300 is scaled to 300 and an HV Score that is more than 900 isscaled to 900.

FIG. 10 depicts an exemplary web page with an HV Score that is providedto an entity, such as lender 1500. The computing platform 4300 mayprovide the HV Score to the lender 1500 through the communicationchannel 1400. As illustrated in FIG. 10, the HV Score may provide thelender 1500 with an indication of whether the property appraisal islikely to be faulty. By way of example only, FIG. 10 depicts that an HVScore below 500 maybe considered at “highest risk” of being faulty,suggesting to the lender 1500 that a review of the appraisal may beappropriate. A review of the appraisal may include a second appraisal,such as an in-person appraisal or automated valuation model appraisal.An HV Score between 500-600 may be considered at “moderate risk” ofbeing faulty, suggesting to the lender 1500 that it conduct a review ofcomparable recent home sales and/or an automated valuation modelappraisal. When an HV Score is above 700, the appraisal is at “lowestrisk” of being faulty, suggesting to the lender 1500 that no furtherreview or verification of the appraisal is necessary.

In one embodiment, the computing platform 4300 may also generate themodel, such as the HV Score model. FIG. 11 shows a flow chart depictingthe steps associated with generating the HV Score model. The computingplatform 4300 may begin by receiving historical (or truth) information(step 11100); determining one or more coefficients (or weights) for theHV Score model based on the received historical information (step11200); and ends when it provides the HV Score model (steps11300-11400).

The computing platform 1300 may receive historical information for oneor more loans from sources of information, such as database 4600 or theinformation source 1800. The historical information may include borrowerinformation, demographic information, loan information, and/or propertyinformation. Moreover, the historical information may includeinformation that is considered reliable and, preferably, verified (e.g.,“truth” data).

In one aspect of the invention, the computing platform 4300 usesappraisal information that is reliable and verified. For example, afirst appraisal performed on a property may be verified by a secondappraisal, such as an in-person appraisal. If a second appraisalconfirms the validity of the first appraisal, the first appraisal thusserves as historical information that is reliable and verified. Althoughunreliable and unverified data may also be used, the quality of the HVScore model may be improved by using reliable and verified data.

In one aspect of the invention, the computing platform 4300 may alsoreceive from a source of information (e.g., database 4600 or informationsource 1800) one or more of the following information that may serve ashistorical information: borrower credit information (e.g., credithistory), a credit score, a credit card balance, a credit card limit,and a ratio of a credit card balance to a credit card limit; aborrower's mortgage loan size; a borrower's car loan size; a borrower'sdelinquencies, such as 30, 60, or 90-day delinquencies (e.g., past duepayments on debt); a median (or average) income for a region, such as astreet, a neighborhood, a city, a ZIP code, a county, a state, acountry, a census tract, and/or a metropolitan statistical area; anindication of whether the borrower is a first time home buyer; a typeassociated with the loan, such as whether the loan is for a purchase, arefinance, or a cash-out refinance; a loan-to-value ratio for borrower'smortgage loan; a borrowers current home value; an indication of whetherthe mortgage loan is secured by a condominium, a single family home, atown house, a 2-4 unit dwelling, a multifamily dwelling, a home in aplanned community; a number indicating the quantity of wage earners inthe borrower's household; a number of co-borrowers; a number indicatingthe quantity of residential units on a property; and/or any otherinformation that may contribute to generating an HV Score model.Moreover, the computing platform 4300 may receive the historicalinformation for a plurality of loans.

To determine the coefficients (step 11200), the computing platform 4300may process the historical information received in step 11100 based onstatistical techniques, such as a logistic regression. By usingstatistical techniques, the computing platform 4300 may determine thecorresponding coefficients (or weights) of the HV Score model. Referringagain to FIG. 9, the exemplary HV Score model lists coefficientsincluding the following: 696.7000, +1.1513, +0.7011, −1.4889, +816.3115,−11.0280, +1.4715, +1.1859, −4.2848, −53.3393, −34.6074, +34.6074,−13.7633, +108.19, +67.90, +0, +0, +0, +0, −79.06, and +114.55. Thecomputing platform 4300 thus uses a statistical technique to determineeach of these coefficients.

In one embodiment, the computing platform 4300 may use a statisticaltechnique referred to as logistic regression to determine thecoefficients. Logistic regression models may be used to examine howvarious factors influence a binary outcome. An event (or result) thathas two possible outcomes is a binary outcome (e.g., good/bad orfaulty/reliable). Logistic modeling is available with many statisticalsoftware packages. For example, the commercially available statisticalpackages offered by SAS Institute Inc. include logistic regressionmodeling tools.

FIG. 12 shows an exemplary flow chart with steps for using a logisticregression approach. The logistic regression approach permitsdetermining coefficients for the HV Score model based on historicalinformation corresponding to one or more loans. Referring to FIG. 12,the computing platform 4300 may verify the first appraisal (step 12100);determine the outcome for each loan based on the historical informationand the verification of the appraisal (step 12200); determine thelikelihood (or probability) associated with each outcome (step 12300);determine one or more coefficients (or weights) for the HV Score model(step 12400); and adjust the one or more parameters by scaling the oneor more coefficients (or the estimated log odds/probability) into arange (step 12500).

To verify the first appraisal (step 12100), the computing platform 4300may compare the first appraisal with a second appraisal. Based on thecomparison, the computing platform 4300 may verify whether the firstappraisal is valid. For example, if the second appraisal is lower thanthe first appraisal, the first appraisal may be considered invalid. Onthe other hand, a second appraisal that is equal to or greater than thefirst appraisal may be considered valid. This second appraisal may be anin-person-appraisal, an AVM appraisal (or estimate), a comparison withcomparable recent sales, and/or any other appraisal of the propertyvalue. FIG. 13 shows an exemplary table showing received historicalinformation for loans with a second appraisal (shown as a “verifiedappraisal”). In this example, the computing platform 4300 may request asecond appraisal for each of the mortgage loans listed in FIG. 13 byeither requesting an in-person appraisal or requesting an AVM appraisal.

To determine the outcome for each mortgage loan (step 12200), thecomputing platform 4300 may compare the second appraisal to the firstappraisal. If the second appraisal is lower than the first appraisal,the computing platform 4300 may set the outcome to a “1.” An outcome of“1” may suggest that the first appraisal is faulty. On the other hand,if the second appraisal is equal to or greater than the first appraisal,the computing platform 4300 may set the outcome to “0.” An outcome of“0” may suggest that the first appraisal is likely to be true. Referringagain to FIG. 13, the computing platform 4300 thus processes each loanto determine an outcome based on the first appraisal and the verifiedsecond appraisal, storing the information depicted in FIG. 13 in thedatabase 4600.

FIG. 13 also depicts a loan number, a first appraisal value, a verifiedappraisal value, an outcome, a loan-to-value ratio, a P factor (seebelow), credit information (e.g., a credit score or history), acondominium flag, and a cash out refinance flag. A skilled artisan wouldrecognize that additional information may also be received by thecomputing platform 4300 to determine the coefficients of the HV Scoremodel including any other information that provides an indication of anappraisal being faulty. For example, the additional information maycorrespond to the variables listed in Table 2.

The loan-to-value shown in FIG. 13 is the ratio of the loan amount tothe fair market value of the property multiplied by 100. The P Factormay be determined based on the following equation:

$\begin{matrix}{{P\mspace{14mu}{Factor}} = \frac{{LOG}\mspace{14mu}\left( {{appraisal}\mspace{14mu}{{value}/{AVM}}\mspace{14mu}{value}} \right)}{{AVM}\mspace{14mu}{standard}\mspace{14mu}{deviation}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$where LOG represents the natural logarithm; the appraisal valuerepresents the first appraisal; the AVM value represents an appraisalvalue provided by an AVM; and the AVM standard deviation represents thestandard deviation of the appraisal values provided by the AVM, such asHVE.

To determine the likelihood for each of the possible outcomes (step12300), the computing platform 4300 may further process the historicalinformation, using a logistic regression, to determine the odds that anoutcome is possible. For example, the computing platform 4300 maydetermine the likelihood that an appraisal value is faulty given itsloan-to-value, first appraisal, verified appraisal, loan-to-value ratio,P-Factor, credit score, condominium flag, and cash out refinance flag.

In one embodiment, the computing platform 4300 uses the followingequation to determine the odds, or likelihood that an outcome, such as afaulty appraisal, is possible:Log(p/1−p))=a+b ₁(LTV)+b ₂(P Factor)+b ₃(Credit Score)+b ₄(Condo Flag)+b₅(Cash Out Refinance Flag)+ . . . b _(n)(n ^(th) Variable)  Equation 4where Log(p/(1−p)) represents the log odds (also referred to as LOGIT)that the appraisal value is likely to be faulty; p represents theprobability of a loan having a “0” outcome (or a “1” outcome); a, b₁,b₂, . . . b_(n) represent the initial coefficients of the HV Scoremodel; and n represents the number of coefficients used in the HV Scoremodel, where b_(n) represents the n^(th) coefficient. Before thecomputing platform 4300 utilizes a logistic regression, the values of a,b₁, b₂, . . . b_(n), and p may be unknown.

In this example, the computing platform 1500 uses five coefficients(i.e., n=5) corresponding to the following five variables: LTV, Pfactor, credit score, condo flag, and cash out refinance flag. Althoughthis example uses five coefficients, a skilled artisan would recognizethat additional coefficients and corresponding variables may be usedinstead.

Although p is an unknown value at the start of the logistic regression,p may conform to the following equation:p=1/(1+e ^(τ))  Equation 5where τ is the following:τ=a+b ₁*LTV+b ₂ *P Factor+b ₃*Credit Score+b ₄*Condo Flag++b ₅*Cash OutRefinance Flag+ . . . b _(n)*Other variable(s).  Equation 6

The computing platform 4300 may then determine an estimate of thecoefficients of the HV Score model (step 12400). That is, the computingplatform 4300 may solve for an estimate of a, b₁, b₂ . . . b_(n) usingequations 4-6.

Although the computing platform 4300 may utilize a logistic regressionapproach as described in this example, a skilled artisan would recognizethat any other approach may be used instead to determine thecoefficients, such as the Probit regression approach available from SASInstitute Inc., standard regression, neural networks, and any otherstatistical or quantitative approach that may provide coefficients basedon historical information (or “truth” data).

Referring again to FIG. 12, to adjust the one or more parameters (step12500), the computing platform 1500 may then scale the coefficients a,b₁, b₂, . . . b_(n). In one embodiment, the computing platform 4300 mayscale the coefficients by multiplying each coefficient by the followingequation:actual coefficient=initial coefficient*(60/ln(2))  Equation 7where ln is a natural logarithm. By using equation 7, the computingplatform 1500 may scale the initial coefficients such that every 60 HVScore points doubles the odds that an appraisal is likely to be faulty.The scaled coefficients may be used as the actual coefficients used inthe HV Score model, such as the HV Score model illustrated in FIG. 9.Accordingly, the computing platform 4300 may determine one or morecoefficients for the HV Score model based on a logistic regressionapproach using historical (or “truth”) information. The computingplatform 4300 may then use the HV Score model to determine the HV Score.

Forecast Data Repository Embodiment

FIG. 14 shows a block diagram of a system consistent with an embodimentof the present invention. System 14000 includes a computing platform4300 with program code 14115, a property database 14120, a forecast datarepository 14130, an archive database 14150, a user terminal 14140, andcommunication channels 1400. System 14000 functions to providehistorical (or “as of”) property value estimates using only informationas of the requested date.

System 14000 accesses, from property database 14120, an address batchthat represents property data of a portion of the entire country. System14000 also provides the property addresses and related data for variousstatistical models, so that program 14115 may compute a currentautomated valuation model (AVM) estimate of property values. Adescription of AVMs and related property databases and statisticalmodels may be found in the above referenced applications, such as U.S.application Ser. Nos. 09/115,831, 09/134,161, 08/730,289 and 09/728,061.One of ordinary skill in the art may use any one or a combination ofmethods to compute the property value estimates without departing fromthe spirit of this invention. Regardless of what method or a combinationof methods are used, the current property value estimates are stored inforecast data repository 14130, while as of estimates are stored inarchive database 14150.

Computing platform 4300 may include a mainframe for managing propertydata and a UNIX machine for processing property data to compute AVMestimates. The mainframe may be an Amdahl processor primarily using tapestorage in addition to having approximately 10 Gigabytes of DASD (DirectAccess Storage Device). The UNIX-specific instructions may be executedon a Sun SPARCT™ 1000e with the Solaris™ 2.5.1 operating system, threesystem boards, six CPUs, 768 Megabytes of memory, and 306 Gigabytes ofdisk space. One skilled in the art may, however, use any computingsystem with adequate processing and memory capabilities.

Alternatively, computing platform 4300 may include a UNIX machine formanaging and processing the property data to compute property valueestimates. The UNIX-specific instructions may be executed on a SunUltrasparc™ E6500 with the Solaris™ version 2.8 operation system, eightsystem boards, ten CPUs at 400 Megahertz, 10 Gigabytes of memory, and 2Terabytes of usable disk storage. One skilled in the art may, however,use any computing system with adequate processing and memorycapabilities. One of ordinary skill in the art will recognize thatvarious computer platforms and operating systems could be used toimplement the present invention, and that it is desirable to upgrade thecomputer system as equipment with increased capability becomesavailable.

Computing platform 4300 may also include program 14115 for controllingthe overall process of computing platform 4300. Moreover, program 14115may be written using such tools as COBOL II, SAS, and IBM utilities(Syncsort, JCL, FTP, etc.). To perform some standard functions, program14115 may use various commercial software such as Group 1 (Code 1 Plusv1.5 and Demographics v2.7), MathSoft (S-Plus v3.4), and the SASInstitute (mainframe and UNIX v6.12).

Property database 14120 may store data representing property data andsales transaction data for use in computing the AVM property valueestimate. The property database 14120 may be periodically updated with abatch of addresses that include property and sales data. Forecast datarepository 14130 may store current AVM estimates, while archive database14150 may store historical AVM estimates.

User terminal 14140 may be implemented as a dummy terminal or a personalcomputer connected to computing platform 4300. Through user terminal14140, users can access preprocessed AVM property value estimates and/orreceive HV scores for any given property at any given date, includingthe current date and the as-of date.

Program 14115 may compute the estimate of property values usingstatistical models, e.g., a repeat sales model and a hedonic model, andcombines the results to produce the best AVM estimate. Other models mayalso be used to compute the AVM property estimates. An example ofcombining results of the statistical models is provided in theabove-referenced related application Ser. No. 08/730,289. Forexplanatory purposes only, the described implementation will combine theproperty value estimates of a repeat sales model and a hedonic model.One skilled in the art may use any one or a combination of methodswithout departing from the spirit of this invention. Regardless of whatmodel or a combination of models are used, the current AVM propertyvalue estimates are stored in forecast data repository 14130 forretrieval before the next predetermined update. One skilled in the artmay also design a system to store the property value estimates inproperty database 14120, which stores the property data. As forecastdata repository 14130 is created, values are added to archive database14150, which serves to store historical information from forecast datarepository 14130.

In one embodiment consistent with the present invention, before new AVMproperty value estimates are generated by periodic updating, theestimates contained in the superceded forecast data repository 14130 arearchived in a database 14150. Alternatively, the forecast datarepository 14130 is copied to archive database 14150 soon after newproperty value estimates are generated by periodic updating, so thatboth the forecast data repository 14130 and the archive database 14150contain a copy of the current estimates. In another implementation, theentire contents of the current forecast data repository 14130 is notarchived, but instead only selected data, such as each property'saddress, estimate value(s), and date of estimate, are archived. In thisimplementation, the archived data may be added to an existing table orother structure in database 14150 containing previously archived data.

In these implementations, the forecast data repository data areorganized in the archive database 14150 according to the dates or timeperiod that the archived forecast data repository covers, for example,Jan. 1, 1999, through Mar. 31, 1999. Thus, archived forecast datarepository data is accessible by specifying a date within the range ofdates that the forecast data repository covers. One of ordinary skillwill recognize that archive database 14150 could be implemented invarious ways without departing from the principles of the presentinvention. For example, archive database 14150 need not necessarily beon a separate storage device, but instead could be implemented on thesame storage device as forecast data repository 14130 or propertydatabase 14120. As another example, one of ordinary skill will alsorecognize that archive database 14150 need not necessarily be a separatedatabase, but instead could be integrated as a part of property database14120 or forecast data repository 14130.

FIG. 15 is a flowchart illustrating a process for determining an AVMproperty value estimate as of a user-specified past date consistent withthe principles of the present invention. As shown in FIG. 15, at thebeginning of the process, a user inputs the date for which an AVMestimate is desired, for example, a mortgage loan origination date (step15050). Next, the user inputs the address of the subject property forwhich the AVM estimate is desired, for example, the address of theproperty that secures the mortgage loan (step 15100). One of ordinaryskill will recognize that the date and address could be specified a vastnumber of ways without departing from the principles of the presentinvention. For instance, the input data could be specified by a computerfile containing the mortgage loan origination dates and subject propertyaddresses corresponding to each loan in a portfolio of 1,000 loans thata financial institution is evaluating for purchase.

In step 15150, the archived forecast data repository (FDR) datacorresponding to the entered date is located. For example, if forecastdata repositories are archived every three months, then the repositorydata for the three months containing the loan origination date enteredin step 15050 is located. Next, the located forecast data repositorydata is searched for an entry corresponding to the address of thesubject property entered in step 15100 (step 15200). When the properentry is found, the price estimate(s) for the subject property as of theentered date is retrieved from the forecast data repository archive(step 15250). One of ordinary skill will recognize that the forecastdata repository data could be organized and accessed in a vast number ofways without departing from the principles of the present invention. Forinstance, entire forecast data repositories could be stored, or just thedate, address, and historical estimate(s) may be archived. The archiveddata could be organized into a single table indexed by property addressand estimate date range or in multiple tables, one for each date range.One of ordinary skill will realize that in a single-tableimplementation, the past estimate corresponding to the address andtarget date could be retrieved using a simple table lookup.

Finally, the process presents the retrieved estimate to the user (step15300). Again, one of ordinary skill will recognize that the retrievedestimate could be presented in a vast number of ways without departingfrom the principles of the present invention. For instance, the estimatecould be displayed on a screen or added to a computer file or databasethat contains similar estimates corresponding to each loan in aportfolio of 1,000 loans that a financial institution is consideringpurchasing. In another example, the estimate could be piped as input toan application program for further processing.

Additional systems and methods consistent with the principles of thepresent invention may rely on an accurate AVM property value estimate asof a past date to evaluate various transactions, functions, and entitiesinvolving the subject property. For example, a financial institutionthat owns the mortgage loan associated with a subject property, such asthe loan originator or a secondary-market purchaser of the loan, may usean independent contemporaneous estimate to perform a quality controlreview of the loan's quality at the time of its origination. The degreeof quality of a mortgage loan is based in no small part on the accuracyof the assessed value of the underlying property. Thus, to fairlydetermine the loan's quality at the time of its origination, a fair andaccurate independent estimate of the underlying property's value at thetime of origination is needed for comparison purposes. If theindependent estimate is biased by the influence of information availableonly after the origination date (information which was unknowable at thetime the loan was made and which may reflect changes in the condition ofthe property subsequent to loan origination), then the independentestimate may not serve as a fair or accurate basis for comparison.

Starting with a comparison of an accurate, independent, contemporaneousestimate to the actual original estimate or assessment used to originatea loan (or for some other transaction based at least in part on thevalue of the subject property), a financial institution (or other party)can improve its business practices and achieve quality control goals.For example, a loan owner can accurately evaluate a loan for possibleappraisal inflation or outright appraisal fraud and flag the loan for afurther formal appraisal review process. This is especially importantfor non-performing loans, i.e., loans in default, because if fraud isfound the current loan owner may be able to recover some of thenon-performing loan losses from the fraud perpetrator. Applied on alarger scale, accurately determining the degree of quality of theoriginal appraisals for a large portfolio of loans enables a financialinstitution to more accurately evaluate the degree of credit-relatedrisk associated with loan default for the entire portfolio.Specifically, if comparison of the original appraised property valueswith accurate contemporaneous estimates reveals that a portfolio'ssecuring properties were worth significantly less than the originalappraisals indicated, then the portfolio owner has a greater risk oflosing money when defaults occur because foreclosure sales of theproperties would generate less money than indicated by the originalappraisals. With an accurate idea of the degree of default risk for aportfolio of loans, a financial institution can better negotiate theportfolio purchase price or even avoid purchasing the portfolio orindividual loans therein. In a related application, a financialinstitution can use accurate as-of-date estimates to perform targetedappraisal reviews on performing loans that it already owns and discoverunknown risk prior to the loans becoming non-performing. For instance,if the loans are found to be riskier than indicated by the originalappraisals, the financial institution could implement hedging strategiesto offset expected default losses, such as purchasing GuaranteeCertificates as described in U.S. patent application Ser. No.09/602,254, assigned to Freddie Mac. As another example, the financialinstitution may acquire insurance, indemnification, or a partial refundfrom the entity that sold the loans. Conversely, the financialinstitution may avoid paying for unnecessary hedges if the comparisonindicates that the loans are safer than expected and deserve a highdegree of confidence.

In yet another application, comparison of original estimates withaccurate as-of-date estimates allow secured loan holders to identifyorigination lenders who have delivered loans with high qualityappraisals, and corresponding lower default-related risk, during thecourse of business. Similarly, lenders who have delivered loans with lowquality appraisals are also identified. With this quality patterninformation, a financial institution or other secured loan purchaser canbetter choose which lenders to purchase loans from, better negotiateloan purchase prices, and negotiate indemnification for past purchasesfrom low-quality lenders.

FIG. 16 is a flowchart illustrating a process that uses a property valueestimate as of a user-specified past date to classify a loan's originalproperty value estimate. First, the process involves determining anindependent property value estimate as of the loan origination date(step 16050). In one implementation consistent with the principles ofthe present invention, this determination is made using a processsimilar to the process shown in FIG. 15. Next, the original propertyvalue estimate used as part of the loan origination process is input(step 16100). One of ordinary skill in the art will recognize that theinput data could be specified a vast number of ways without departingfrom the principles of the present invention. For instance, in animplementation that uses a computer to perform the process, the originalproperty value estimate could be read by a human from the loanorigination papers and typed in. As another example, it could be part ofa read-in computer file containing loan information, including theoriginal property value estimate, corresponding to each loan in aportfolio of 1,000 loans.

The process also involves comparing the as-of-date estimate (from step16050) to the original estimate (from step 16100) and determiningwhether the two estimates are within ten percent of each other (step16150). If the as-of-date estimate and the original estimate are withinten percent of each other, then the process classifies the associatedloan as having an ordinary degree of risk (step 16200), reports the riskassessment classification (step 16400), and ends. One of ordinary skillwill recognize that the threshold risk classification value of tenpercent used in this example could be set higher or lower, or adjusteddynamically, without departing from the principles of the presentinvention. For instance, the step 16150 threshold for classification asan ordinary risk load could be set at five percent or fifteen percent.

Referring again to step 16150, if the as-of-date estimate and theoriginal estimate are not within ten percent of each other, then adetermination is made as to whether the as-of-date estimate is less thanthe original property value estimate (step 16250). If so, (i.e., theoriginal property value estimate is more than ten percent higher thanthe as-of-date estimate), then the process classifies the loan as havinga high degree of risk (step 16300), flags the loan for investigation ofthe originator and original assessor (step 16350), reports the riskassessment classification (step 16400), and ends. One of ordinary skillwill realize that the numerous other actions could be taken instead of,or in addition to, flagging the high-risk loan for investigation withoutdeparting from the principles of the present invention. For instance, ifthe loan was part of a portfolio of loans being offered for sale, theloan could be excluded from the portfolio because the buyer does notwish to purchase high-risk loans. In another example, the assessor whomade the original over-inflated property value estimate could beidentified, and then other loans that originally utilized the sameassessor could be selectively chosen for closer scrutiny. In yet anotherexample, the originating lender who made the mortgage loan based on theoriginal over-inflated property value estimate could be identified,contacted, and asked to indemnify the current loan holder against lossesup to a limit based on the difference in the estimates. Referring againto step 16250, if the as-of-date estimate is greater than the originalestimate, then the loan is classified as having a low degree of risk(step 16450), the risk assessment classification is reported (step16400), and the process ends. One of ordinary skill will realize thatreporting the risk assessment classification could be done in myriadways without departing from the principles of the present invention. Forinstance, the classification may simply be displayed to a user on acomputer screen, who then makes decisions based on the classificationthat improve business or help achieve quality control goals. In anotherexample, the classification may be written to a database according toloan identification information such as property address and date.Alternatively, the classification may be reported by digitallytransmitting it to an application program that uses it to evaluatevarious transactions and functions involving the subject property. Inaddition, one of ordinary skill will realize that more or fewer thanthree risk classifications could be used without departing from theprinciples of the present invention.

Instead of using a percentage difference between the as-of-date estimateand the original estimate, in one embodiment, the HV score isdetermined. The HV Score may serve as an indication that the originalestimate of property value was faulty.

The systems herein may be embodied in various forms including, forexample, a data processor, such as the computer that also includes adatabase. Moreover, the above-noted features and other aspects andprinciples of the present invention may be implemented in variousenvironments. Such environments and related applications may bespecially constructed for performing the various processes andoperations of the invention or they may include a general-purposecomputer or computing platform selectively activated or reconfigured bycode to provide the necessary functionality. The processes disclosedherein are not inherently related to any particular computer or otherapparatus, and may be implemented by a suitable combination of hardware,software, and/or firmware. For example, various general-purpose machinesmay be used with programs written in accordance with teachings of theinvention, or it may be more convenient to construct a specializedapparatus or system to perform the required methods and techniques.

Systems and methods consistent with the present invention also includecomputer readable media that include program instruction or code forperforming various computer-implemented operations based on the methodsand processes of the invention. The media and program instructions maybe those specially designed and constructed for the purposes of theinvention, or they may be of the kind well known and available to thosehaving skill in the computer software arts. Examples of programinstructions include for example machine code, such as produced by acompiler, and files containing a high level code that can be executed bythe computer using an interpreter.

Furthermore, although the embodiments above refer to processinginformation related to mortgage loans secured by improved real property,systems and methods consistent with the present invention may processinformation related to other types of loans or credit instruments,including those secured by property, such as automobiles and/or personalproperty. Moreover, although reference is made herein to using the HVScore to assess a residential property for a mortgage loan, in itsbroadest sense systems and methods consistent with the present inventionmay provide a score for any type of property including commercialproperty.

1. A computer-implemented method for classifying an existing loansecured by a property into one or more risk categories, thecomputer-implemented method comprising: retrieving a property estimateperformed on the property, the property estimate used to originate theexisting loan secured by the property; determining a time period whenthe property estimate was performed on the property; retrieving anautomated value estimate of the property as of the time period when theproperty estimate was performed, the automated value estimate beingdetermined using an automated valuation model (AVM); calculating, usinga computing platform, a score reflecting whether the property estimateaccurately reflected the value of the property as of the time periodwhen the property estimate was performed, the score being calculatedbased on a comparison between the property estimate and the automatedvalue estimate; and categorizing the existing loan into one of the riskcategories based on the calculated score.
 2. A system for classifying anexisting loan secured by a property into one or more risk categories,the system comprising: at least one non-transitory memory comprisingcomputer executable instructions to: retrieve a property estimateperformed on the property, the property estimate used to originate theexisting loan secured by the property; determine a time period when theproperty estimate was performed on the property; retrieve an automatedvalue estimate of the property as of the time period when the propertyestimate was performed, the automated value estimate being determinedusing an automated valuation model (AVM); calculating a score reflectingwhether the property estimate accurately reflected the value of theproperty as of the time period when the property estimate was performed,the score being calculated based on a comparison between the propertyestimate and the automated value estimate; and categorize the loan intoone of the risk categories based on the calculated score, and at leastone data processor that executes the instructions.
 3. A non-transitorycomputer-readable medium comprising computer executable code for causinga processor to execute a method for classifying an existing loan securedby a property into one or more risk categories, the method comprising:retrieving a property estimate performed on the property, the propertyestimate used to originate the existing loan secured by the property;determining a time period when the property estimate was performed onthe property; retrieving an automated value estimate of the property asof the time period when the property estimate was performed, theautomated value estimate being determined using an automated valuationmodel (AVM); calculating a score reflecting whether the propertyestimate accurately reflected the value of the property as of the timeperiod when the property estimate was performed, the calculated scorebeing generated based on a comparison between the property estimate andthe automated value estimate; and categorizing the loan into one of therisk categories based on the calculated score.
 4. Thecomputer-implemented method of claim 1, wherein the existing loan ispart of a portfolio of loans.
 5. The computer-implemented method ofclaim 4, wherein the risk categories indicate a risk to an owner of theportfolio of loans in case of default of the existing loan.
 6. Thecomputer-implemented method of claim 1, wherein the time period is anorigination date of the existing loan.
 7. The computer-implementedmethod of claim 1, wherein the property estimate was part of anorigination process for the existing loan.
 8. The computer-implementedmethod of claim 1, further comprising: determining that the existingloan is high risk if the property estimate is greater than the automatedvalue estimate by more than a threshold amount.
 9. Thecomputer-implemented method of claim 8, further comprising: identifyinga party that provided the property estimate, if the existing loan isdetermined to be high risk.
 10. The system of claim 2, wherein theexisting loan is part of a portfolio of loans.
 11. The system of claim10, wherein the risk categories indicate a risk to an owner of theportfolio of loans in case of default of the existing loan.
 12. Thesystem of claim 2, wherein the time period is an origination date of theexisting loan.
 13. The system of claim 2, wherein the property estimatewas part of an origination process for the existing loan.
 14. The systemof claim 2, the memory further comprising instructions to: determinethat the existing loan is high risk if the property estimate is greaterthan the automated value estimate by more than a threshold amount. 15.The system of claim 14, the memory further comprising instructions to:identify a party that provided the property estimate, if the existingloan is determined to be high risk.
 16. The computer-readable medium ofclaim 3, wherein the existing loan is part of a portfolio of loans. 17.The computer-readable medium of claim 16, wherein the risk categoriesindicate a risk to an owner of the portfolio of loans in case of defaultof the existing loan.
 18. The computer-readable medium of claim 3,wherein the time period is an origination date of the existing loan. 19.The computer-readable medium of claim 3, wherein the property estimatewas part of an origination process for the existing loan.
 20. Thecomputer-readable medium of claim 3, the method further comprising:determining that the existing loan is high risk if the property estimateis greater than the automated value estimate by more than a thresholdamount.
 21. The computer-readable medium of claim 20, the method furthercomprising: identifying a party that provided the property estimate, ifthe existing loan is determined to be high risk.